Optimisation of Temperature Fields of Microsystems with Self-Organising Neural Nets

Thermal modelling and optimisation of parameter distributed systems is a rather time-consuming process. In this paper the problem of optimisation of temperature fields of VLSI circuits and systems is attacked by a selforganising neural net. The net directly solves the task generated by a heuristic a...

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Main Authors: P. Bratek, A. Kos
Format: Article
Language:English
Published: Wiley 2003-01-01
Series:Active and Passive Electronic Components
Online Access:http://dx.doi.org/10.1080/1042015031000073850
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author P. Bratek
A. Kos
author_facet P. Bratek
A. Kos
author_sort P. Bratek
collection DOAJ
description Thermal modelling and optimisation of parameter distributed systems is a rather time-consuming process. In this paper the problem of optimisation of temperature fields of VLSI circuits and systems is attacked by a selforganising neural net. The net directly solves the task generated by a heuristic algorithm. No physical model of thermal phenomena is used. The proposed method is simple. Some examples and statistical results are presented. The proposed method is addressed mostly to large, high-speed system designs.
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institution Kabale University
issn 0882-7516
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publishDate 2003-01-01
publisher Wiley
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series Active and Passive Electronic Components
spelling doaj-art-a2a21e35a9e24ed984f3dd4c81f989f82025-08-20T03:38:44ZengWileyActive and Passive Electronic Components0882-75161563-50312003-01-0126314114910.1080/1042015031000073850Optimisation of Temperature Fields of Microsystems with Self-Organising Neural NetsP. Bratek0A. Kos1Institute of Electronics, University of Mining and Metallurgy, Mickiewicza 30, Kraków 30-059, PolandInstitute of Electronics, University of Mining and Metallurgy, Mickiewicza 30, Kraków 30-059, PolandThermal modelling and optimisation of parameter distributed systems is a rather time-consuming process. In this paper the problem of optimisation of temperature fields of VLSI circuits and systems is attacked by a selforganising neural net. The net directly solves the task generated by a heuristic algorithm. No physical model of thermal phenomena is used. The proposed method is simple. Some examples and statistical results are presented. The proposed method is addressed mostly to large, high-speed system designs.http://dx.doi.org/10.1080/1042015031000073850
spellingShingle P. Bratek
A. Kos
Optimisation of Temperature Fields of Microsystems with Self-Organising Neural Nets
Active and Passive Electronic Components
title Optimisation of Temperature Fields of Microsystems with Self-Organising Neural Nets
title_full Optimisation of Temperature Fields of Microsystems with Self-Organising Neural Nets
title_fullStr Optimisation of Temperature Fields of Microsystems with Self-Organising Neural Nets
title_full_unstemmed Optimisation of Temperature Fields of Microsystems with Self-Organising Neural Nets
title_short Optimisation of Temperature Fields of Microsystems with Self-Organising Neural Nets
title_sort optimisation of temperature fields of microsystems with self organising neural nets
url http://dx.doi.org/10.1080/1042015031000073850
work_keys_str_mv AT pbratek optimisationoftemperaturefieldsofmicrosystemswithselforganisingneuralnets
AT akos optimisationoftemperaturefieldsofmicrosystemswithselforganisingneuralnets